To address this drawback, recent studies propose hardware-aware NAS, which incorporates the hardware constraints of networks during the search process [1]}. Nevertheless, current approaches are time-consuming since they involve training the candidate network, and a tedious search process [2]}.
To accelerate NAS, recent NAS approaches rely on graph neural networks (GNNs) to estimate the accuracy of a given network [3]}, [4]}, [5]}, [6]}. However, training a GNN-based accuracy predictor is still time-consuming (in the order of tens of minutes [7]} to hours [8]} on GPU clusters).
Therefore, adapting existing NAS approaches to different hardware architecture is challenging due to their intensive computation and execution time requirements.
